Information-Theoretic Distribution Test with Application to Normality

Thanasis Stengos and Ximing Wu (wuximing05@gmail.com)

No 604, Working Papers from University of Guelph, Department of Economics and Finance

Abstract: We derive general distribution tests based on the method of Maximum Entropy density. The proposed tests are derived from maximizing the di®erential entropy subject to moment constraints. By exploiting the equivalence between the Maximum Entropy and Maximum Likelihood estimates of the general exponential family, we can use the conventional Likelihood Ratio, Wald and Lagrange Multiplier testing principles in the maximum entropy framework. In particular, we use the Lagrange Multiplier method to derive tests for normality and their asymptotic properties. Monte Carlo evidence suggests that the proposed tests have desirable small sample properties and often outperform commonly used tests such as the Jarque-Bera test and the Kolmogorov-Smirnov-Lillie test for normality. We show that the proposed tests can be extended totests based on regression residuals and non-iid data in a straightforward manner. We apply the proposed tests to the residuals from a stochastic production frontier model and reject the normality hypothesis.

Keywords: distribution test; maximum entropy; normality. (search for similar items in EconPapers)
JEL-codes: C1 C12 C16 (search for similar items in EconPapers)
Pages: 23 pages
Date: 2006
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (5)

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Related works:
Journal Article: Information-Theoretic Distribution Test with Application to Normality (2010)
Working Paper: Information-Theoretic Distribution Test with Application to Normality (2007)
Working Paper: Information-Theoretic Distribution Test with Application to Normality (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:gue:guelph:2006-4

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